Data Mining and Business Intelligence (Includes Practicals)
ISBN: 9789351197188
380 pages
eBook also available for institutional users
Description
The book introduces the concept of data mining as an important tool for enterprise data management and as a cutting edge technology for building competitive advantage. The readers will be able to effectively identify sources of data and process it for data mining and become well versed in all data mining algorithms, methods and tools. The coverage of the book will help you to analytically identify opportunities to derive business value from data.
Chapter 1: Introduction to Data Mining
1.1 Definition of Data Mining
1.2 How Does Data Mining Work?
1.3 Architecture of Data Mining
1.4 Kinds of Data that can be mined
1.5 Data Mining Functionalities
1.6 Types of Data Mining Systems
1.7 Advantages of Data Mining
1.8 Disadvantages of Data Mining
1.9 Ethical Issues in Data Mining
Chapter 2: Data Exploration
2.1 Data
2.2 Data Visualization
Chapter 3: Data Preprocessing
3.1 Why Preprocessing?
3.2 Data Cleaning
3.3 Data Integration
3.4 Data Reduction
3.5 Data Transformation
3.6 Data Discretization and Concept Hierarchy Generation
Chapter 4: Classification
4.1 Basic Concepts
4.2 Classification Methods
4.3 Prediction
4.4 Model Evaluation and Selection
4.5 Combining Classifiers (Ensemble Methods)
Chapter 5: Clustering
5.1 Introducing Cluster Analysis
5.2 Clustering Methodologies
Chapter 6: Outlier Analysis
6.1 Real-World Applications
6.2 Types of Outliers
6.3 Outlier Challenges
6.4 Outlier Detection Approaches
6.5 Outlier Detection Methods
6.6 Proximity-Based Outlier Analysis
6.7 Clustering-Based Outlier Analysis
Chapter 7: Frequent Pattern Mining
7.1 Market Basket Analysis
7.2 Efficient and Scalable Frequent Item set Mining Methods
7.3 Mining Multilevel and Multidimensional Association Rules
7.4 Association Mining to Correlation Analysis
Chapter 8: Introduction to Business Intelligence
8.1 Data, Information and Knowledge
8.2 Defining Business Intelligence
8.3 Important Factors in Business Intelligence
8.4 Business Intelligence Architecture
8.5 Business Intelligence Framework
8.6 Role of Mathematical Models in BI
8.7 Factors Responsible for a Successful BI Project
8.8 Development of BI System
8.9 Obstacles to Business Intelligence in an Organization
8.10 Ethics and Business Intelligence
Chapter 9: Decision Support System
9.1 Concept of Decision Making
9.2 Techniques of Decision Making
9.3 Understanding Decision Support System (DSS)
9.4 Evolution of Information System
9.5 Development of Decision Support System
9.6 Application of DSS
9.7 Role of Business Intelligence in Decision Making
Chapter 10: BI and Data Mining Applications
10.1 ERP and Business Intelligence
10.2 BI Applications in CRM
10.3 BI Applications in Marketing
10.4 BI Applications in Logistics and Production
10.5 Role of BI in Finance
10.6 BI Applications in Banking
10.7 BI Applications in Telecommunications
10.8 BI Applications in Fraud Detection
10.9 BI Applications in Clickstream Mining
10.10 BI Applications in the Retail Industry
Summary
Review Exercise
Multiple Choice Questions
Descriptive Questions
Practicals
Index